Epilepsy Detection

Epilepsy detection research focuses on developing accurate and reliable methods for identifying epileptic seizures from brainwave data, primarily using electroencephalography (EEG) and increasingly stereoelectroencephalography (SEEG). Current efforts concentrate on advanced machine learning models, including convolutional neural networks (CNNs), transformers, and hybrid architectures that leverage both local and global features within EEG signals, often incorporating techniques like wavelet transforms and scattering transforms for improved feature extraction. These advancements aim to improve diagnostic accuracy, enable continuous patient monitoring, and facilitate personalized treatment strategies, ultimately enhancing the lives of individuals affected by epilepsy.